&lt;title&gt;Prefetching: PACS image management optimization using HIS/RIS information&lt;/title&gt;

Lodder, H.; Poppel, Bas M. van; Bakker, A. R.

doi:10.1117/12.45276

Cited by 9 publications

(2 citation statements)

References 5 publications

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“… 220 , 221 Hospital information system (HIS)-RIS integration with PACS were again a common theme describing image management optimization. 222 – 225 Radiologists’ opinions on the use of PACS were studied 226 , 227 and a paper demonstrating a digital radiologic teaching file was presented. 228 …”

Section: S Pacs Conferencesmentioning

confidence: 99%

SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

Andriole

2022

J. Med. Imag.

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Section: S Pacs Conferencesmentioning

confidence: 99%

SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

Andriole

2022

J. Med. Imag.

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“…Current retrieval schemes from most picture archiving and communication systems (PACS) are based on structured patient demographic and procedural information (1,2). These data are automatically extracted from the Digital Imaging and Communication in Medicine (DICOM) headers and included in indexes that facilitate retrieval of the relevant images.…”

Section: Introductionmentioning

confidence: 99%

Image Content Extraction: Application to MR Images of the Brain

Sinha

Ton²,

Yaghmai³

et al. 2001

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A system for automatically extracting image content features was developed that combines registration to a labeled atlas with natural language processing of free-text radiology reports. The system was then tested with T1-weighted, spoiled gradient-echo magnetic resonance (MR) imaging studies of the brain performed in nine patients. The locations of 599 structures were visually assessed by an experienced radiologist and compared with the locations indicated by automated output. The in-plane accuracy of the contours was subjectively evaluated as either good, moderate, or poor. The criterion for classifying a structure as correctly located was that 90% or more of all the images containing the structure had to be correctly identified. For 98% of the structures, the images identified by the automated algorithm agreed with those identified by the radiologist, and in 83% of cases, image contours showed a good in-plane overlap. The results of this validation study demonstrate that this combination of registration and natural language processing is accurate in identifying relevant images from brain MR imaging studies. However, the range of applicability of this technique has yet to be determined by applying the technique to a large number of studies.

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